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Overview of HADOOP

When we look into the structure of this framework , we find that it is divided into HDFS and MapReduce.
The two hands of Hadoop framework
i.e. HDFS and MapReduce are Hadoop Distributed File System- the storage hand and the processing hand respectively. This skeleton divides the total data into smaller blocks and circulate them in assemblage through the junctions called nodes in the network. The JAR is sent to the nodes where the data needs to be worked on. Here the nodes responsible for those data residing near them work faster in transferring them.

big-data
Hadoop  carries  four programs  of  study.

They  are :
-> Hadoop Common
-> HDFS
-> Hadoop YARN
-> Hadoop MapReduce

1. Hadoop common acts as the information centre that contains the collections of Hadoop libraries.
2. HDFS is the storage part of high band of frequencies.
3.The YARN organizes the properties that line up the user’s function in an assemblage.
4.MapReduce is the processor that processes all sets of data.

Hadoop is such a frame that can store and circulate huge data files without minimum error. Hence it is highly scalable.

1.This software costs very less for storing and performing computations on data in comparison with the traditional databases.
2.Accessing with different kinds of business solution data is done by Hadoop with ultimate comfort. It has been proving to be its best in decision making.
3.It helps in social media, emailing, log processing, data warehousing, error detection etc.
4. Since it maps the data wherever it is placed so Hadoop takes very less time in order to unfold any data. It hardly takes an hour to work on large petabytes of data. Hene, it is super fast.

Hence, the companies using Hadoop are able to gain far better insights. Whenever a data block goes from one node to another in the assemblage of the network, each  block gets copied to each node and even if the data is lost, we will always be having a backup copy of it. Hence, fault occurrence is really very low.

 

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